Abstract
A traditional interpolation model is characterized by the choice of regularizer applied to the interpolant, and the choice of noise model. Typically, the regularizer has a single regularization constant α, and the noise model has a single parameter β. The ratio α/β alone is responsible for determining globally all these attributes of the interpolant: its ‘complexity’, ‘flexibility’, ‘smoothness’, ‘characteristic scale length’, and ‘characteristic amplitude’. We suggest that interpolation models should be able to capture more than just one flavour of simplicity and complexity. We describe Bayesian models in which the interpolant has a smoothness that varies spatially. We emphasize the importance, in practical implementation, of the concept of ‘conditional convexity’ when designing models with many hyperparameters.
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References
Blake, A., and Zisserman, A. (1987) Visual Reconstruction. Cambridge Mass.: MIT Press.
Gilks, W., and Wild, P. (1992) Adaptive rejection sampling for Gibbs sampling. Applied Statistics41: 337 – 348.
Gu, C., and Wahba, G. (1991) Minimizing GCV/GML scores with multiple smoothing parameters via the Newton method. SIAM J. Sci. Stat. Comput.12: 383 – 398.
Gull, S. F. (1988) Bayesian inductive inference and maximum entropy. In Maximum Entropy and Bayesian Methods in Science and Engineering, vol. 1: Foundations, ed. Gull, S. F, pp. 53 – 74, Dordrecht. Kluwer.
Kimeldorf, G. S., and Wahba, G. (1970) A correspondence between Bayesian estimation of stochastic processes and smoothing by splines. Annals of Mathematical Statistics41 (2): 495 – 502.
Lewicki, M. (1994) Bayesian modeling and classification of neural signals. Neural Computation6 (5): 1005 – 1030.
MacKay, D. J. C. (1992) Bayesian interpolation. Neural Computation4 (3): 415 – 447.
MacKay, D. J. C. (1994) Hyperparameters: Optimize, or integrate out? In Maximum Entropy and Bayesian Methods, Santa Barbara 1993, ed. by G. Heidbreder, Dordrecht. Kluwer.
MacKay, D. J. C, and Takeuchi, R., (1994) Interpolation models with multiple hyperparameters. Submitted to IEEE PAMI.
Muller, H. G., and Stadtmuller, U. (1987) Variable bandwidth kernel estimators of regression-curves. Annals of Statistics15 (1): 182 – 201.
Smith, A. (1991) Bayesian computational methods. Philosophical Transactions of the Royal Society of London A337: 369 – 386.
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© 1996 Kluwer Academic Publishers
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MacKay, D.J.C., Takeuchi, R. (1996). Interpolation Models with Multiple Hyperparameters. In: Skilling, J., Sibisi, S. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 70. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0107-0_27
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DOI: https://doi.org/10.1007/978-94-009-0107-0_27
Publisher Name: Springer, Dordrecht
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